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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 1126–1150 of 1277 papers

TitleStatusHype
Differentiable Greedy Networks—0
Differentiable Scaffolding Tree for Molecular Optimization—0
Differentiable Scaffolding Tree for Molecule Optimization—0
Differentially Private Partial Set Cover with Applications to Facility Location—0
DIFFRAC: a discriminative and flexible framework for clustering—0
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits—0
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions—0
Directed percolation and numerical stability of simulations of digital memcomputing machines—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Discrepancy-based Evolutionary Diversity Optimization—0
Discrete graphical models -- an optimization perspective—0
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos—0
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations—0
Diversity from Human Feedback—0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize—0
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms—0
Duality between Feature Selection and Data Clustering—0
D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis—0
Dynamic Algorithms for Matroid Submodular Maximization—0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization—0
Dynamic Assortment Optimization with Changing Contextual Information—0
Dynamic Feature Selection for Dependency Parsing—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
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